Advertisement

ZDM

, Volume 49, Issue 4, pp 531–544 | Cite as

How to make ‘more’ better? Principles for effective use of multiple representations to enhance students’ learning about fractions

  • Martina A. Rau
  • Percival G. Matthews
Original Article

Abstract

To make complex mathematics concepts accessible to students, teachers often rely on visual representations. Because no single representation can depict all aspects of a mathematics concept, instruction typically uses multiple representations. Much research shows that multiple representations can have immense benefits for students’ learning. However, some re-search also cautions that multiple representations may fail to enhance students’ learning if they are not used in the “right” way. For example, unless students can (1) properly interpret each individual representation and (2) make connections among multiple representations and the information they intend to convey, the use of multiple representations may actually confuse students rather than aid their learning. In this article, we review research-based principles for how to use multiple representations effectively so that they enhance student learning. Using fractions as an illustrative domain, we discuss how the choice of visual representation may affect student learning based on the conceptual aspects of the to-be-learned content emphasized by the representation. Next, we describe ways to help students interpret individual representations and to make connections among them. We illustrate these arguments with our own empirical research on fractions learning. We conclude by laying out open questions that future research should address.

Keywords

Fractions Multiple representations Conceptual learning Perceptual learning 

References

  1. Acevedo Nistal, A., Van Dooren, W., Clarebout, G., Elen, J., & Verschaffel, L. (2009). Conceptualising, investigating and stimulating representational flexibility in mathematical problem solving and learning: a critical review. The International Journal on Mathematics Education, 41(5), 627–636.Google Scholar
  2. Ainsworth, S. (2006). Deft: A Conceptual Framework for Considering Learning with Multiple Representations. Learning and Instruction, 16(3), 183–198. doi: 10.1016/j.learninstruc.2006.03.001.CrossRefGoogle Scholar
  3. Ainsworth, S., Bibby, P., & Wood, D. (2002). Examining the Effects of Different Multiple Representational Systems in Learning Primary Mathematics. Journal of the Learning Sciences, 11(1), 25–61. doi: 10.1207/S15327809JLS1101_2.CrossRefGoogle Scholar
  4. Ainsworth, S., & Loizou, A. (2003). The effects of self-explaining when Learning with text or diagrams. Cognitive Science: A Multidisciplinary Journal, 27(4), 669–681.CrossRefGoogle Scholar
  5. Airey, J., & Linder, C. (2009). A disciplinary discourse perspective on university science learning: Achieving fluency in a critical constellation of modes. Journal of Research in Science Teaching, 46(1), 27–49. doi: 10.1002/tea.20265.CrossRefGoogle Scholar
  6. Behr, M. J., Post, T. R., Harel, G., & Lesh, R. (1993). Rational Numbers: Toward a Semantic Analysis - Emphasis on the Operator Construct. In T. P. Carpenter, E. Fennema & T. A. Romberg (Eds.), Rational Numbers: An Integration of Research. Hillsdale: Lawrence Erlbaum Associates.Google Scholar
  7. Berthold, K., Eysink, T. H. S., & Renkl, A. (2008). Assisting self-explanation prompts are more effective than open prompts when learning with multiple representations. Instructional Science, 27(4), 345–363. doi: 10.1007/s11251-008-9051-z.Google Scholar
  8. Berthold, K., & Renkl, A. (2009). Instructional Aids to support a conceptual understanding of multiple representations. Journal of Educational Research, 101(1), 70–87. doi: 10.1037/a0013247.Google Scholar
  9. Bodemer, D., & Faust, U. (2006). External and mental referencing of multiple representations. Computers in Human Behavior, 22(1), 27–42. doi: 10.1016/j.chb.2005.01.005.CrossRefGoogle Scholar
  10. Boyer, T. W., & Levine, S. C. (2012). Child proportional scaling: Is 1/3= 2/6= 3/9= 4/12? Journal of Experimental Child Psychology, 111(3), 516–533.CrossRefGoogle Scholar
  11. Boyer, T. W., Levine, S. C., & Huttenlocher, J. (2008). Development of proportional reasoning: Where young children go wrong. Developmental psychology, 44(5), 1478–1490.CrossRefGoogle Scholar
  12. Charalambous, C. Y., & Pitta-Pantazi, D. (2007). Drawing on a theoretical model to study students’ understandings of fractions. Educational Studies in Mathematics, 64(3), 293–316. doi: 10.1007/s10649-006-9036-2.CrossRefGoogle Scholar
  13. Chi, M. T. H., Feltovitch, P. J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive science, 5, 121–152. doi: 10.1207/s15516709cog0502_2.CrossRefGoogle Scholar
  14. Cramer, K., & Wyberg, T. (2009). Efficacy of different concrete models for teaching the part-whole construct for fractions. Mathematical Thinking and Learning, 11(4), 226–257. doi: 10.1080/10986060903246479.CrossRefGoogle Scholar
  15. Cramer, K., Wyberg, T., & Leavitt, S. (2008). The role of representations in fraction addition and subtraction. Mathematics teaching in the middle school, 13(8), 490.Google Scholar
  16. de Croock, M. B. M., Van Merrienboër, J. J. G., & Paas, F. G. W. C. (1998). high versus low contextual interference in simulation-based training of troubleshooting skills: Effects on transfer performance and invested mental effort. Computers in Human Behavior, 14(2), 249–267.CrossRefGoogle Scholar
  17. DeLoache, J. S. (2000). Dual representation and young children’s use of scale models. Child Development, 71(2), 329–338. doi: 10.1111/1467-8624.00148.CrossRefGoogle Scholar
  18. diSessa, A. A. (2004). Metarepresentation: Native competence and targets for instruction. Cognition and Instruction, 22(3), 293–331.CrossRefGoogle Scholar
  19. diSessa, A. A., & Sherin, B. L. (2000). Meta-representation: An introduction. The Journal of Mathematical Behavior, 19(4), 385–398. doi: 10.1016/S0732-3123(01)00051-7.CrossRefGoogle Scholar
  20. Fabbri, S., Caviola, S., Tang, J., Zorzi, M., & Butterworth, B. (2012). The role of numerosity in processing nonsymbolic proportions. The Quarterly Journal of Experimental Psychology, 65(12), 2435–2446.CrossRefGoogle Scholar
  21. Gentner, D., & Markman, A. B. (1997). Structure Mapping in analogy and similarity. American Psychologist, 52(1), 45–56. doi: 10.1037/0003-066X.52.1.45.CrossRefGoogle Scholar
  22. Gibson, E. J. (2000). Perceptual learning in development: Some basic concepts. Ecological Psychology, 12(4), 295–302. doi: 10.1207/S15326969ECO1204_04.CrossRefGoogle Scholar
  23. Harden, R. M., & Stamper, N. (1999). What is a spiral curriculum? Medical Teacher, 21(2), 141–143.CrossRefGoogle Scholar
  24. Huttenlocher, J., Duffy, S., & Levine, S. (2002). Infants and toddlers discriminate amount: Are they measuring? Psychological Science, 13(3), 244–249.CrossRefGoogle Scholar
  25. Jacob, S. N., & Nieder, A. (2009). Notation-independent representation of fractions in the human parietal cortex. Journal of Neuroscience, 29(14), 4652–4657.CrossRefGoogle Scholar
  26. Jacob, S. N., Vallentin, D., & Nieder, A. (2012). Relating magnitudes: The brain’s code for proportions. Trends in Cognitive Sciences, 16(3), 157–166.CrossRefGoogle Scholar
  27. Jeong, Y., Levine, S. C., & Huttenlocher, J. (2007). The development of proportional reasoning: Effect of continuous versus discrete quantities. Journal of Cognition and Development, 8(2), 237–256.CrossRefGoogle Scholar
  28. Kellman, P. J., & Massey, C. M. (2013). Perceptual learning, cognition, and expertise. In B. H. Ross (Ed.), The psychology of learning and motivation (Vol. 558, pp. 117–165). New York: Elsevier Academic Press.Google Scholar
  29. Kieren, T. E. (1993). Rational and fractional numbers: From quotient fields to recursive understanding. In T. P. Carpenter, E. Fennema & T. A. Romberg (Eds.), Rational numbers: an integration of research. Hillsdale: Erlbaum.Google Scholar
  30. Koedinger, K. R., Corbett, A. T., & Perfetti, C. (2012). The knowledge-learning-instruction framework: Bridging the science-practice chasm to enhance robust student learning. Cognitive science, 36(5), 757–798. doi: 10.1111/j.1551-6709.2012.01245.x.CrossRefGoogle Scholar
  31. Lewis, M. R., Matthews, P. G., & Hubbard, E. M. (2015). Neurocognitive architectures and the nonsymbolic foundations of fractions understanding. In D. B. Berch, D. C. Geary & K. M. Koepke (Eds.), Development of mathematical cognition: Neural substrates and genetic influences (pp. 141–160): Elsevier.Google Scholar
  32. Mack, N. (1995). Confounding whole-number and fraction concepts when building on informal knowledge. Journal for Research in Mathematics Education, 26(5), 422–441.Google Scholar
  33. Massey, C. M., Kellman, P. J., Roth, Z., & Burke, T. (2011). Perceptual learning and adaptive learning technology—developing new approaches to mathematics learning in the classroom. In N. L. Stein & S. W. Raudenbush (Eds.), Developmental cognitive science goes to school (pp. 235–249). New York: Routledge.Google Scholar
  34. Matthews, P. G., & Chesney, D. L. (2015). Fractions as percepts? Exploring cross-format distance effects for fractional magnitudes. Cognitive Psychology, 78, 28–56. doi: 10.1016/j.cogpsych.2015.01.006.CrossRefGoogle Scholar
  35. Matthews, P. G., & Lewis, M. R. (2017). Fractions we can’t ignore: The ratio congruity effect. Cognitive Science. doi: 10.1111/cogs.12419.Google Scholar
  36. McCrink, K., & Wynn, K. (2007). Ratio abstraction by 6-month-old infants. Psychological Science, 18(8), 740–745.CrossRefGoogle Scholar
  37. NCTM. (2000). Principles and Standards for School Mathematics. Reston: National Council of Teachers of Mathematics.Google Scholar
  38. NCTM. (2006). Curriculum Focal Points for Prekindergarten through Grade 8 Mathematics: A Quest for Coherence. Reston, VA.Google Scholar
  39. Ni, Y., & Zhou, Y.-D. (2005). Teaching and learning fraction and rational numbers: The origins and implications of whole number bias. Educational Psychologist, 40(1), 27–52.CrossRefGoogle Scholar
  40. NMAP (2008). Foundations for success: Report of the National Mathematics Advisory Board Panel: U.S. Government Printing Office.Google Scholar
  41. NRC. (2006). Learning to think spatially. Washington, DC: National Academies Press.Google Scholar
  42. Ohlsson, S. (1988). Mathematical meaning and applicational meaning in the semantics of fractions and related concepts. Number concepts and operations in the middle grades, 2, 53–92.Google Scholar
  43. Post, T. R., Behr, M. J., & Lesh, R. (1982). Interpretations of rational number concepts. In L. Silvey & J. R. Smart (Eds.), Mathematics for the middle grades (5-9). Reston, VA: National Council of Teachers of Mathematics.Google Scholar
  44. Rau, M. A. (2016). Conditions for the effectiveness of multiple visual representations in enhancing stem learning. Educational Psychology Review, 1–45. doi: 10.1007/s10648-016-9365-3.
  45. Rau, M. A., Aleven, V., & Rummel, N. (2013). Interleaved practice in multi-dimensional learning tasks: Which dimension should we interleave? Learning and Instruction, 23, 98–114. doi: 10.1016/j.learninstruc.2012.07.003.CrossRefGoogle Scholar
  46. Rau, M. A., Aleven, V., & Rummel, N. (2015). Successful learning with multiple graphical representations and self-explanation prompts. Journal of Educational Psychology, 107(1), 30–46. doi: 10.1037/a0037211.CrossRefGoogle Scholar
  47. Rau, M. A., Aleven, V., & Rummel, N. (2016). Supporting students in making sense of connections and in becoming perceptually fluent in making connections among multiple graphical representations. Journal of Educational Psychology. doi: 10.1037/edu0000145.Google Scholar
  48. Rau, M. A., Aleven, V., Rummel, N., & Pardos, Z. (2014). How should intelligent tutoring systems sequence multiple graphical representations of fractions? A multi-methods study. International Journal of Artificial Intelligence in Education, 24(2), 125–161. doi: 10.1007/s40593-013-0011-7.CrossRefGoogle Scholar
  49. Renkl, A. (2005). The Worked-out Example Principle in Multimedia Learning. In R. Mayer (Ed.), The Cambridge handbook of multimedia learning (pp. 229–246). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  50. Schnotz, W. (2005). An Integrated Model of Text and Picture Comprehension. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (pp. 49–69). New York: Cambridge University Press.CrossRefGoogle Scholar
  51. Seufert, T. (2003). Supporting coherence formation in learning from multiple representations. Learning and Instruction, 13(2), 227–237. doi: 10.1016/S0959-4752(02)00022-1.CrossRefGoogle Scholar
  52. Shanks, D. (2005). Implicit learning. In K. Lamberts & R. Goldstone (Eds.), Handbook of cognition (pp. 202–220). London: Sage.Google Scholar
  53. Siegler, R. S., Carpenter, T., Fennell, F., Geary, D., Lewis, J., Okamoto, Y., … Wray, J. (2010). Developing effective fractions instruction: A practice guide. Washington, DC: National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education.Google Scholar
  54. Siegler, R. S., Thompson, C. A., & Schneider, M. (2011). An integrated theory of whole number and fractions development. Cognitive Psychology, 62(4), 273–296.CrossRefGoogle Scholar
  55. Singer, F. M. (2007). Beyond conceptual change: using representations to integrate domain—specific struct ural models in learning mathematics. Mind, Brain, and Education, 1(2), 84–97.CrossRefGoogle Scholar
  56. Singer, F. M. (2009). The dynamic infrastructure of mind—a hypothesis and some of its applications. New Ideas in Psychology, 27, 48–74.CrossRefGoogle Scholar
  57. Stern, E., Aprea, C., & Ebner, H. G. (2003). Improving cross-content transfer in text processing by means of active graphical representation. Learning and Instruction, 13(2), 191–203. doi: 10.1016/S0959-4752(02)00020-8.CrossRefGoogle Scholar
  58. Sweller, J. (1990). Cognitive load as a factor in the structuring of technical material. Journal of Experimental Psychology; General, 119(2), 176–192. doi: 10.1037//0096-3445.119.2.176.CrossRefGoogle Scholar
  59. Taber, K. S. (2013). Revisiting the chemistry triplet: drawing upon the nature of chemical knowledge and the psychology of learning to inform chemistry education. Chemistry Education Research and Practice, 14(2), 156–168. doi: 10.1039/C3RP00012E.CrossRefGoogle Scholar
  60. Tversky, B. (2011). Visualizing thought. Topics in Cognitive Science, 3(3), 499–535. doi: 10.1111/j.1756-8765.2010.01113.x.CrossRefGoogle Scholar
  61. Tversky, B., Zacks, J., Lee, P., & Heiser, J. (2000). Lines, Blobs, Crosses and Arrows: Diagrammatic Communication with Schematic Figures. In M. Anderson, P. Cheng & V. Haarslev (Eds.), International conference on theory and application of diagrams (pp. 221–230). Berlin/Heidelberg: Springer.CrossRefGoogle Scholar
  62. Uttal, D. H., & O’Doherty, K. (2008). Comprehending and learning from ‘visualizations’: a developmental perspective. In J. Gilbert (Ed.), Visualization: Theory and practice in science education (pp. 53–72). Netherlands: Springer.CrossRefGoogle Scholar
  63. Vallentin, D., & Nieder, A. (2008). behavioral and prefrontal representation of spatial proportions in the monkey. Current Biology, 18(8), 1420–1425.CrossRefGoogle Scholar
  64. van der Meij, J., & de Jong, T. (2006). Supporting students’ learning with multiple representations in a dynamic simulation-based learning environment. Learning and Instruction, 16(3), 199–212. doi: 10.1016/j.learninstruc.2006.03.007.CrossRefGoogle Scholar
  65. van der Meij, J., & de Jong, T. (2011). The Effects of directive self-explanation prompts to support active processing of multiple representations in a simulation-based learning environment. Journal of Computer Assisted Learning, 27(5), 411–423. doi: 10.1111/j.1365-2729.2011.00411.x.CrossRefGoogle Scholar
  66. Yang, Y., Hu, Q., Wu, D., & Yang, S. (2015). Children’s and adults’ automatic processing of proportion in a Stroop-like task. International Journal of Behavioral Development, 39(2), 97–104.CrossRefGoogle Scholar

Copyright information

© FIZ Karlsruhe 2017

Authors and Affiliations

  1. 1.University of Wisconsin MadisonMadisonUSA

Personalised recommendations